Controllable Image Enhancement
This work addresses the challenge of fitting subtle and changeable user preferences in photo enhancement, which is incremental as it builds on existing encoder-decoder and ISP methods.
The authors tackled the problem of subjective user preferences in automated image enhancement by developing a semiautomatic algorithm that generates high-quality images with multiple styles using a few parameters, achieving state-of-the-art performance on benchmark datasets for image quality and model efficiency.
Editing flat-looking images into stunning photographs requires skill and time. Automated image enhancement algorithms have attracted increased interest by generating high-quality images without user interaction. However, the quality assessment of a photograph is subjective. Even in tone and color adjustments, a single photograph of auto-enhancement is challenging to fit user preferences which are subtle and even changeable. To address this problem, we present a semiautomatic image enhancement algorithm that can generate high-quality images with multiple styles by controlling a few parameters. We first disentangle photo retouching skills from high-quality images and build an efficient enhancement system for each skill. Specifically, an encoder-decoder framework encodes the retouching skills into latent codes and decodes them into the parameters of image signal processing (ISP) functions. The ISP functions are computationally efficient and consist of only 19 parameters. Despite our approach requiring multiple inferences to obtain the desired result, experimental results present that the proposed method achieves state-of-the-art performances on the benchmark dataset for image quality and model efficiency.